Global Feature Fusion Attention Network For Single Image Dehazing

As a common low-level visual task, image dehazing is widely used in various multimedia fields. However, it faces great challenges due to its ill-posed property, especially the problem with dense haze removal. In this paper, we propose an efficient end-to-end global feature fusion attention network for single image dehazing based on an encoder-decoder structure. We design a Multi-scale Global Context Fusion (MGCF) block to capture the global context feature and integrates them into the network to assist in the recovery of the dense haze area. After the global feature is aggregated in the context modeling module, a two-stream structure is used to combine two feature transformation modes and two feature fusion modes to obtain more comprehensive and accurate features. We simplify the pixel attention block and combine it with the MGCF block to form a Global Feature Fusion Attention (GFFA) module. Based on the local residual learning and the GFFA module, the Feature Enhancement (FE) module is proposed as the basic module of the encoder-decoder, allowing the network to focus on enhancing more useful feature. Experimental results show that the proposed method surpasses state-of-the-art dehazing methods, both quantitatively and qualitatively.